Read-across can increase confidence in the Next Generation Risk Assessment for skin sensitisation: A case study with resorcinol

A problem formulation framework for the application of in silico toxicology methods in chemical risk assessment

The first step in the hazard or risk assessment of chemicals should be to formulate the problem through a systematic and iterative process aimed at identifying and defining factors critical to the assessment. However, no general agreement exists on what components an in silico toxicology problem formulation (PF) should include. The present work aims to develop a PF framework relevant to the application of in silico models for chemical toxicity prediction. We modified and applied a PF framework from the general risk assessment literature to peer reviewed papers describing PFs associated with in silico toxicology models. Important gaps between the general risk assessment literature and the analyzed PF literature associated with in silico toxicology methods were identified. While the former emphasizes the need for PFs to address higher-level conceptual questions, the latter does not. There is also little consistency in the latter regarding the PF components addressed, reinforcing the need for a PF framework that enable users of in silico toxicology models to answer the central conceptual questions aimed at defining components critical to the model application. Using the developed framework, we highlight potential areas of uncertainty manifestation in in silico toxicology PF in instances where particular components are missing or implicitly described. The framework represents the next step in standardizing in silico toxicology PF component. The framework can also be used to improve the understanding of how uncertainty is apparent in an in silico toxicology PF, thus facilitating ways to address uncertainty.

Non-animal approaches for photoallergenicity safety assessment: Needs and perspectives for the toxicology for the 21st century

Certain chemicals and/or their byproducts are photoactivated by UV/VIS and trigger a dermal allergenic response, clinically recognized as photoallergic contact dermatitis (PACD). It is important to identify the chemicals which are potentially photoallergenic, not only for establishing the correct differential diagnosis between PACD and other photodermatoses, but also as causative agents which should be avoided as a preventative measure. Moreover, materials with photoallergenic properties need to be correctly identified to allow thorough safety assessments for their use in finished products (e.g. cosmetics). Development of methods for predicting photoallergenicity potential of chemicals has advanced at slow pace in recent years. To date, there are no validated methods for photosensitisation potential of chemicals for regulatory purposes, although it remains a required endpoint in some regions. The purpose of this review is to explore the mechanisms potentially involved in the photosensitisation process and discuss the methods available in the literature for identification of photosensitisers. The review also explores the possibilities of further research investment required to develop human-relevant new approach methodologies (NAMs) and next generation risk assessment (NGRA) approaches, considering the current perspectives and needs of the Toxicology for the 21st Century.

Skin sensitisation prediction using read-across, an illustrative next generation risk assessment (NGRA) case study for vanillin

Skin sensitisation is a key adverse human health effect to be addressed in the safety assessment of cosmetic ingredients. Regulatory demands and scientific progress have led to the development of a Next Generation Risk Assessment (NGRA) framework, relying on the use of New Approach Methodologies (NAM) Defined Approaches (DA) and read-across instead of generating animal data. This case study illustrates the application of read-across for the prediction of the skin sensitisation potential of vanillin at the hypothetical use concentration of 0.5% in a shower gel and face cream. A three-step process was applied to select the most suitable analogues based on their protein reactivity, structural characteristics, physicochemical properties, skin metabolism profile and availability of skin sensitisation data. The applied read-across approach predicted a weak skin sensitiser potential for vanillin corresponding with a Local Lymph Node Assay EC3 value of 10%. Based on this EC3 value a point of departure of 2500 μg/cm2 was derived, resulting in an acceptable exposure level (AEL) of 25 μg/cm2. Because the consumer exposure levels (CEL) for the face cream (13.5 μg/cm2) and shower gel (0.05 μg/cm2) scenarios were lower than the AEL, the NGRA concluded both uses as safe.

Incorporating integrated testing strategy (ITSv1) defined approach into read-across (RAx) in predicting skin sensitization potency: ITSv1-based RAx

Recently, due to regulatory and ethical demands, new approach methodologies (NAMs), defined approaches (DAs), and read-across (RAx) have been used in the risk assessment of skin sensitization. Integrated testing strategy (ITS)v1 DA, adopted in OECD Guideline No. 497, can be used for skin sensitization potency categorization. However, ITSv1 DA alone is not used for further refinement of the potency prediction based on EC3 (the estimated concentration that produces a stimulation index of 3 in murine local lymph node assay) values. Moreover, there is no explicit approach to incorporating NAM/DA data into RAx to fill the data gap of EC3 values with high confidence. This study developed a strategy incorporating ITSv1 DA into RAx to predict skin sensitization potency: ITSv1-based RAx. To examine the reliability of this novel strategy, a case study with lilial, a fragrance material, was performed. Based on ITSv1-based RAx, the skin sensitization potency of lilial was determined by extrapolating the EC3 value of 9.5% for the suitable analogue bourgeonal, which was close to the historical EC3 value of 8.6%. The result suggested that the strategy can refine the prediction of EC3 values with high confidence and be useful for the risk assessment of skin sensitization.

Decision making in next generation risk assessment for skin allergy: Using historical clinical experience to benchmark risk

Our aim is to develop and apply next generation approaches to skin allergy risk assessment that do not require new animal test data and better quantify uncertainties. Quantitative risk assessment for skin sensitisation uses safety assessment factors to extrapolate from the point-of-departure to an acceptable human exposure level. It is currently unclear whether these safety assessment factors are appropriate when using non-animal test data to derive a point-of departure. Our skin allergy risk assessment model Defined Approach uses Bayesian statistics to infer a human-relevant metric of sensitiser potency with explicit quantification of uncertainty, using any combination of human repeat insult patch test, local lymph node assay, direct peptide reactivity assay, KeratinoSens™, h-CLAT or U-SENS™ data. Here we describe the incorporation of benchmark exposures pertaining to use of consumer products with clinical data supporting a high/low risk categorisation for skin sensitisation. Margins-of-exposure (potency estimate to consumer exposure level ratio) are regressed against the benchmark risk classifications, enabling derivation of a risk metric defined as the probability that an exposure is low risk. This approach circumvents the use of safety assessment factors and provides a simple and transparent mechanism whereby clinical experience can directly feed-back into risk assessment decisions.

Expansion of the Cosmetics Europe skin sensitisation database with new substances and PPRA data

The assessment of skin sensitisation is a key requirement in all regulated sectors, with the European Union's regulation of cosmetic ingredients being most challenging, since it requires quantitative skin sensitisation assessment based on new approach methodologies (NAMs). To address this challenge, an in-depth and harmonised understanding of NAMs is fundamental to inform the assessment. Therefore, we compiled a database of NAMs, and in vivo (human and local lymph node assay) reference data. Here, we expanded this database with 41 substances highly relevant for cosmetic industry. These structurally different substances were tested in six NAMs (Direct Peptide Reactivity Assay, KeratinoSens™, human Cell Line Activation Test, U-SENS™, SENS-IS, Peroxidase Peptide Reactivity Assay). Our analysis revealed that the substances could be tested without technical limitations, but were generally overpredicted when compared to reference results. Reasons for this reduced predictivity were explored through pairwise NAM comparisons and association of overprediction with hydrophobicity. We conclude that more detailed understanding of how NAMs apply to a wider range of substances is needed. This would support a flexible and informed choice of NAMs to be optimally applied in the context of a next generation risk assessment framework, ultimately contributing to the characterisation and reduction of uncertainty.

Implementation of a dermal sensitization threshold (DST) concept for risk assessment: structure-based DST and in vitro data-based DST

Skin sensitization resulting in allergic contact dermatitis represents an important toxicological endpoint as part of safety assessments. When available substance-specific sensitization data are inadequate, the dermal sensitization threshold (DST) concept has been proposed to set a skin exposure threshold to provide no appreciable risk of skin sensitization. Structure-based DSTs, which include non-reactive, reactive, and high potency category (HPC) DSTs, can be applied to substances with an identified chemical structures. An in vitro data-based "mixture DST" can be applied to mixtures based on data from in vitro test methods, such as KeratinoSens™ and the human Cell Line Activation Test. The purpose of this review article is to discuss the practical use of DSTs for conducting sound sensitization risk assessments to assure the safety of consumer products. To this end, several improvements are discussed in this review. For application of structure-based DSTs, an overall structural classification workflow was developed to exclude the possibility that "HPC but non-reactive" chemicals are misclassified as "non-reactive", because such chemicals should be classified as HPC chemicals considering that HPC rules have been based on the chemical structure of high potency sensitizers. Besides that, an extended application of the mixture DST principle to mixtures that either is cytotoxic or evaluated as positive was proposed. On a final note, we also developed workflows that integrate structure-based and in vitro-based mixture DST. The proposed workflows enable the application of the appropriate DST, which serves as a point of departure in the quantitative sensitization risk assessment.

Amending the U-SENS™ skin sensitization test method for interfering auto-fluorescent chemicals

Limitations of the applicability domain of new approach methodologies (NAM) present a major challenge for the testing of cosmetic ingredients in Europe, as the regulation does not allow to resort to in vivo test method. Therefore, research focused on overcoming such limitations of established in vitro test methods is frequently conducted. Here, we address a limitation of the U-SENS™, an in vitro skin sensitization test method that addresses the key event 3 on activation of dendritic cells of the adverse outcome pathway (AOP) for skin sensitization. The applicability domain of the U-SENS™ excludes autofluorescent substances that can interfere with the measurement of the expression of CD86, i.e., the primary readout. An evaluation of several fluorochromes identified APC as most suitable for testing auto-fluorescent chemicals. Acceptance criteria were reproducibly met when using the APC-labelled antibody. Equivalent performance in terms of reproducibility and skin sensitisation hazard assessment of the standard FITC-labelled antibodies and the APC-labelled antibodies was demonstrated by testing 40 substances. Finally, the value of the expanded technical applicability domain was highlighted with a case study using sulfuretin. In conclusion, we successfully demonstrated the expansion of the U-SENS™ applicability domain to interfering auto-fluorescent chemicals by using APC-labelled antibodies.

Next generation risk assessment for skin allergy: Decision making using new approach methodologies

Our aim is to develop and apply next generation approaches to skin allergy risk assessment (SARA) that do not require new animal test data and better quantify uncertainties. Significant progress has been made in the development of New Approach Methodologies (NAMs), non-animal test methods, for assessment of skin sensitisation and there is now focus on their application to derive potency information for use in Next Generation Risk Assessment (NGRA). The SARA model utilises a Bayesian statistical approach to infer a human-relevant metric of sensitiser potency and a measure of risk associated with a given consumer exposure based upon any combination of human repeat insult patch test, local lymph node, direct peptide reactivity assay, KeratinoSens™, h-CLAT or U-SENS™ data. Here we have applied the SARA model within our weight of evidence NGRA framework for skin allergy to three case study materials in four consumer products. Highlighting how to structure the risk assessment, apply NAMs to derive a point of departure and conclude on consumer safety risk. NGRA based upon NAMs were, for these exposures, at least as protective as the historical risk assessment approaches. Through such case studies we are building our confidence in using NAMs for skin allergy risk assessment.

A hypothetical skin sensitisation next generation risk assessment for coumarin in cosmetic products

Next generation Risk Assessment (NGRA) is an exposure-led, hypothesis-driven approach which integrates new approach methodologies (NAMs) to assure safety without generating animal data. This hypothetical skin allergy risk assessment of two consumer products - face cream containing 0.1% coumarin and deodorant containing 1% coumarin - demonstrates the application of our skin allergy NGRA framework which incorporates our Skin Allergy Risk Assessment (SARA) Model. SARA uses Bayesian statistics to provide a human relevant point of departure and risk metric for a given chemical exposure based upon input data that can include both NAMs and historical in vivo studies. Regardless of whether NAM or in vivo inputs were used, the model predicted that the face cream and deodorant exposures were low and high risk respectively. Using only NAM data resulted in a minor underestimation of risk relative to in vivo. Coumarin is a predicted pro-hapten and consequently, when applying this mechanistic understanding to the selection of NAMs the discordance in relative risk could be minimized. This case study demonstrates how integrating a computational model and generating bespoke NAM data in a weight of evidence framework can build confidence in safety decision making.

Repeat-dose toxicity prediction with Generalized Read-Across (GenRA) using targeted transcriptomic data: A proof-of-concept case study

Read-across is a data gap filling technique utilized to predict the toxicity of a target chemical using data from similar analogues. Recent efforts such as Generalized Read-Across (GenRA) facilitate automated read-across predictions for untested chemicals. GenRA makes predictions of toxicity outcomes based on "neighboring" chemicals characterized by chemical and bioactivity fingerprints. Here we investigated the impact of biological similarities on neighborhood formation and read-across performance in predicting hazard (based on repeat-dose testing outcomes from US EPA ToxRefDB v2.0). We used targeted transcriptomic data on 93 genes for 1060 chemicals in HepaRG^™ cells that measure nuclear receptor activation, xenobiotic metabolism, cellular stress, cell cycle progression, and apoptosis. Transcriptomic similarity between chemicals was calculated using binary hit-calls from concentration-response data for each gene. We evaluated GenRA performance in predicting ToxRefDB v2.0 hazard outcomes using the area under the Receiver Operating Characteristic (ROC) curve (AUC) for the baseline approach (chemical fingerprints) versus transcriptomic fingerprints and a combination of both (hybrid). For all endpoints, there were significant but only modest improvements in ROC AUC scores of 0.01 (2.1%) and 0.04 (7.3%) with transcriptomic and hybrid descriptors, respectively. However, for liver-specific toxicity endpoints, ROC AUC scores improved by 10% and 17% for transcriptomic and hybrid descriptors, respectively. Our findings suggest that using hybrid descriptors formed by combining chemical and targeted transcriptomic information can improve in vivo toxicity predictions in the right context.

Next generation risk assessment for skin sensitisation: A case study with propyl paraben

Skin sensitisation is a key adverse health effect to be addressed in the safety assessment of cosmetic ingredients. Regulatory demands have urged the development of Next Generation Risk Assessment (NGRA) using New Approach Methodologies (NAM) and Defined Approaches (DA) instead of animal models. An illustrative NGRA case study shall demonstrate if the use of propyl paraben at 0.2% in a face cream was safe for consumers. A sequential stacking tier testing DA based on NAM data predicted propyl paraben to be a non-sensitiser, while some NAM input data showed positive results. To increase confidence, structurally related parabens were considered, which revealed NAM and DA hazard predictions similar to those of propyl paraben, non-sensitiser classifications in animal models and very rare cases of human skin allergy. Based on a weight of evidence it was decided that propyl paraben should be considered a non-sensitiser leading to a favourable NGRA conclusion, in line with traditional risk assessment. Examination of an ab initio NGRA based on NAM and metabolism data resulted in a more conservative weak sensitiser consideration as point of departure, which still led to a favourable conclusion.

Development of an Enhanced Mechanistically Driven Mode of Action Classification Scheme for Adverse Effects on Environmental Species

This study developed a novel classification scheme to assign chemicals to a verifiable mechanism of (eco-)toxicological action to allow for grouping, read-across, and in silico model generation. The new classification scheme unifies and extends existing schemes and has, at its heart, direct reference to molecular initiating events (MIEs) promoting adverse outcomes. The scheme is based on three broad domains of toxic action representing nonspecific toxicity (e.g., narcosis), reactive mechanisms (e.g., electrophilicity and free radical action), and specific mechanisms (e.g., associated with enzyme inhibition). The scheme is organized at three further levels of detail beyond broad domains to separate out the mechanistic group, specific mechanism, and the MIEs responsible. The novelty of this approach comes from the reference to taxonomic diversity within the classification, transparency, quality of supporting evidence relating to MIEs, and that it can be updated readily.